Quantum Machine Learning: Benefits and Practical Examples

A quantum computer that is useful in practice, is expected to be developed in the next few years. An important application is expected to be machine learning, where benefits are expected on run time, capacity and learning efficiency. In this paper, these benefits are presented and for each benefit an example application is presented. A quantum hybrid Helmholtz machine use quantum sampling to improve run time, a quantum Hopfield neural network shows an improved capacity and a variational quantum circuit based neural network is expected to deliver a higher learning efficiency.

[1]  Alejandro Perdomo-Ortiz,et al.  Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices , 2017, ArXiv.

[2]  S. Lloyd,et al.  Quantum Hopfield neural network , 2017, Physical Review A.

[3]  Maria Schuld,et al.  Supervised Learning with Quantum Computers , 2018 .

[4]  Hans-J. Briegel,et al.  Machine learning \& artificial intelligence in the quantum domain , 2017, ArXiv.

[5]  Steve Mullett,et al.  Read the fine print. , 2009, RN.

[6]  Frank Phillipson,et al.  Quantum Hopfield Neural Networks: A New Approach and Its Storage Capacity , 2020, ICCS.

[7]  N. Meinhardt,et al.  Implementation of a Variational Quantum Circuit for Machine Learning with Compact Data Representation , 2019, Digitale Welt.

[8]  Santosh S. Venkatesh,et al.  The capacity of the Hopfield associative memory , 1987, IEEE Trans. Inf. Theory.

[9]  Travis S. Humble,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[10]  Frank Phillipson,et al.  Hybrid Helmholtz machines: a gate-based quantum circuit implementation , 2020, Quantum Information Processing.

[11]  Hartmut Neven,et al.  Classification with Quantum Neural Networks on Near Term Processors , 2018, 1802.06002.

[12]  John Preskill,et al.  Quantum computing and the entanglement frontier , 2012, 1203.5813.

[13]  H. Nishimori,et al.  Quantum annealing in the transverse Ising model , 1998, cond-mat/9804280.

[14]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[15]  Ulya R. Karpuzcu,et al.  Quantum Computing: An Overview Across the System Stack , 2019 .

[16]  S. Lloyd.  Quantum-Mechanical Computers , 1995 .

[17]  Richard Versluis,et al.  Machine learning in the quantum era , 2019 .

[18]  T. Martinez,et al.  Quantum associative memory with exponential capacity , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).